Technical Medicine (MII) & Applied Mathematics (AI4Health) student @ University of Twente.
Medical Imaging Analysis · Medical Signals Analysis · Deep Learning · (Multimodal) Machine Learning · Reinforcement Learning · Navigation Technology · Statistical Learning · Optimization · Markov Decision Process · Inverse Problems in Imaging
I'm a Technical Medicine and Applied Mathematics student at the University of Twente, with a strong interest in Medical Image Analysis, Medical Signal Analysis, Deep Learning, Machine Learning, and Reinforcement Learning. I focus on building robust deep learning algorithms for medical imaging & biomedical signals analysis. Where I apply technical methods to real clinical problems, especially in image analysis and signal processing.
- Medical Image Analysis: segmentation, registration, and image transformation for clinical applications
- Medical Signal Analysis: analysis of physiological signals such as ECG, EEG, and other signals
- Deep Learning & Machine Learning: building robust models for healthcare and medical imaging
- Reinforcement Learning: decision-making and optimization methods for technical and clinical problems
- Clinical Applications: translating technical methods into practical solutions for real-world healthcare challenges
- Languages: Python · MATLAB · R ·
- Libraries: PyTorch · TensorFlow · MONAI · Jupyter · SimpleITK · nibabel · PyRadiomics · VTK · NumPy · Pandas · Scikit-learn
- Tools: GitHub · Git · LaTeX · 3D Slicer
| Project | Description | Stack |
|---|---|---|
| 🫀 ACDC Segmentation | Developed a MONAI-based cardiac MRI segmentation workflow for the ACDC dataset, with both 2D and 2.5D slice pipelines. Implemented offline preprocessing, preprocessing QC, model training, checkpoint testing, and Optuna hyperparameter search. Used dynamic class weighting and Dice + Cross-Entropy loss to improve segmentation performance on LV, RV, and myocardium. | MONAI · PyTorch · nibabel · SimpleITK |